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Artificial Intelligence in Virtual Design and Construction (VDC): Present Status, Opportunities and Future Challenges

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 10 September 2025 | Viewed by 1744

Special Issue Editors


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Guest Editor
Founding Director and Assistant Professor, HUD Center of Excellence for Innovation in Affordable Housing and Sustainable Communities, Department of Built Environment, North Carolina Agricultural and Technical State University, Greensboro, NC, USA
Interests: artificial intelligence; robotics; computer vision; construction engineering and management; BIM/VDC; affordable housing; smart city
Assistant Professor, Department of Geography and the Environment, The University of Alabama, Tuscaloosa, AL, USA
Interests: GIS; disasters; transportation; public health; sustainability

E-Mail Website
Guest Editor
Chair and Associate Professor, Department of Big Data Management and Application, Southwest University, Chongqing, China
Interests: intelligent decision-making; data optimization decision-making; sustainable development; BIM

Special Issue Information

Dear Colleagues,

This Special Issue aims to collect high-quality review papers from the fields of Virtual Design and Construction research and practices. We encourage researchers from the fields of Construction Science, Technology, Engineering, and Management to contribute review papers that highlight the latest developments in their research field, or to invite relevant experts and colleagues to do so. Topics of interest for this Special Issue include, but are not limited to:

Artificial Intelligence (AI) Technologies

  • Machine Learning, Deep Learning, and Reinforcement Learning;
  • Computer Vision;
  • NLP (Natural Language Processing);
  • Robotics and Unmanned Vehicles;
  • Generative AI.

Innovations in Virtual Design and Construction (VDC)

  • AI in Advancing Construction Surveying and Measurements;
  • AI in Improving Quality of Designs;
  • AI in Virtual Design and Construction;
  • AI in Advancing Estimating, Scheduling and Control;
  • AI in Creating a Safer Jobsite;
  • AI in Assessing and Reducing Risk;
  • AI in Increasing the Project’s Lifespan;
  • AI in Powerful Everyday Automation;
  • AI in Legal Affairs and Dispute Resolution in Engineering and Construction.

Leveraging AI and VDC for Resilience

  • AI in Boosting Disaster Response and Recovery;
  • AI in Smart City and Communities;
  • AI in Affordable Housing;
  • AI in Built Environment Assessment;
  • AI in Enhanced Decision-Making;
  • AI in Connected Transportation and Smart Infrastructure;
  • AI in Construction Materials Research;
  • AI in Finite Element Analysis and Numerical Simulation.

AI Equity: Ensuring Access to AI for All

  • AI in Architecture, Engineering and Construction (AEC) Education;
  • AI in Changing the Field of Workforce Development;
  • AI Data Equity;
  • AI Ethics.

Dr. Yuhan Jiang
Dr. Dapeng Li
Dr. Qingwei Shi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI) technologies
  • innovations in virtual design and construction (VDC)
  • leveraging AI and VDC for resilience
  • AI equity: ensuring access to AI for all

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Published Papers (1 paper)

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Research

15 pages, 3017 KiB  
Article
Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods
by Gumgum Darmawan, Gatot Riwi Setyanto, Defi Yusti Faidah and Budhi Handoko
Appl. Sci. 2025, 15(2), 675; https://doi.org/10.3390/app15020675 - 11 Jan 2025
Viewed by 997
Abstract
The lunar calendar is often overlooked in time-series data modeling despite its importance in understanding seasonal patterns, as well as economics, natural phenomena, and consumer behavior. This study aimed to investigate the effectiveness of the lunar calendar in modeling and forecasting rainfall levels [...] Read more.
The lunar calendar is often overlooked in time-series data modeling despite its importance in understanding seasonal patterns, as well as economics, natural phenomena, and consumer behavior. This study aimed to investigate the effectiveness of the lunar calendar in modeling and forecasting rainfall levels using various machine learning methods. The methods employed included long short-term memory (LSTM) and gated recurrent unit (GRU) models to test the accuracy of rainfall forecasts based on the lunar calendar compared to those based on the Gregorian calendar. The results indicated that machine learning models incorporating the lunar calendar generally provided greater accuracy in forecasting for periods of 3, 4, 6, and 12 months compared to models using the Gregorian calendar. The lunar calendar model demonstrated higher accuracy in its prediction, exhibiting smaller errors (MAPE and MBE values), whereas the Gregorian calendar model yielded somewhat larger errors and tended to underestimate the values. These findings contributed to the advancement of forecasting techniques, machine learning, and the adaptation to non-Gregorian calendar systems while also opening new opportunities for further research into lunar calendar applications across various domains. Full article
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